How to Use Google Looker
Thinking about using Google Looker but feeling a bit overwhelmed? You're not alone. Looker is a powerhouse for business intelligence, but its professional-grade features can seem intimidating at first. This guide will walk you through the core concepts and show you how to start building your first reports and dashboards, step-by-step.
What Exactly is Google Looker?
Google Looker (often just called Looker) is a business intelligence platform that helps businesses explore, analyze, and visualize their data. Think of it as a layer that sits on top of your company's database, allowing you to ask questions and get answers in the form of charts, graphs, and reports without needing to write complex SQL code every time.
But here's what makes Looker different from many other BI tools: it's built around a powerful data modeling language called LookML. This is both its greatest strength and its biggest hurdle for newcomers.
Understanding the Role of LookML
Instead of having every user connect to raw data tables and potentially create inconsistent metrics, Looker uses a centralized model built by a data analyst or developer. This LookML model defines all your business logic in one place.
For example, the data team can define exactly what constitutes "Revenue," "Active Users," or "Conversion Rate." This means when someone on the marketing team and someone in sales pull a report on revenue, they are guaranteed to be looking at the same number calculated the same way. It enforces consistency across the entire organization.
The downside? You, as a business user, can't just connect a new data source and go. You are working with the pre-built models your data team has created for you. This makes it incredibly reliable but less flexible for quick, one-off analyses without a developer's help.
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Getting Familiar with the Looker Interface
When you first log in to Looker, the interface is clean but can be a bit cryptic. Let’s break down the main components you'll interact with daily.
- Folders: This is how Looker organizes content. You'll typically have your personal folder for saving scratch work, a Shared folder for team reports, and other folders set up by your organization.
- Explores: This is your starting point for analysis. An "Explore" is a curated view of your data, built with LookML, that lets you mix and match different data points to build a query. Think of it as a question-building sandbox.
- Looks: A "Look" is a single, saved visualization. It’s a snapshot of a specific query and its accompanying chart or table. For example, a single line chart showing website traffic over the last 30 days would be saved as a Look.
- Dashboards: A dashboard is simply a collection of Looks (visualizations) arranged on a single page. This gives you a high-level overview of different metrics all in one place.
How to Build Your First Report in Looker
Ready to get your hands dirty? Let's build a simple report from scratch using an "Explore." This is the core workflow every Looker user needs to know.
Step 1: Choose Your "Explore"
Your journey begins in the Explore section. Your Looker administrator or data team will have created a set of Explores for you to use. These are usually named intuitively, like "Users," "Order Items," or "Website Traffic."
For this example, let's imagine we want to see user-base growth over time. We'd start by choosing an Explore named something like Users.
Step 2: Select Dimensions and Measures
This is where the magic happens. The Explore interface presents you with two primary types of fields in the left sidebar:
- Dimensions: These are the "group by" fields. They are attributes you can use to slice and dice your data. Examples include dates (Created Date, Last Active Year), categories (Country, Traffic Source), or text fields (User Name, Email Domain).
- Measures: These are the numbers you want to calculate. They are aggregate functions like counts, sums, or averages. Common examples are Count of Users, Average Order Value, or Total Revenue.
To build our report on user growth, we would select:
- A dimension for the time period, like "Created Date > Month".
- A measure for the number of users, like "User Count".
As you click on these fields, Looker automatically adds them to your query builder.
Step 3: Add Filters to Narrow Your Data
Right now, our query would pull all user data from all time. That's probably too much. Filters let you focus on a specific segment.
The "Filters" section is at the top of your query builder. You can click on any dimension or measure to filter by it. A common first filter is to set a date range. We could filter our Created Date field to be "in the last 12 months."
You can also filter by categories. For instance, if we only wanted to see users from a specific source, we could add a filter on the "Traffic Source" dimension and set it to match "Google Organic."
Step 4: Run the Query
Once your fields and filters are set, just click the Run button in the top right corner. Looker will write the SQL for your query in the background, run it against your database, and return a data table with the results.
For our example, you'd see a simple table with two columns: one for the "Created Month" and one for the "User Count" in that month.
From Report to Dashboard: Visualizing Your Data
A table of numbers is useful, but a chart is much easier to understand. Here's how to turn your raw data into a visual and add it to a dashboard.
Step 1: Choose a Visualization Type
Below your data table, you'll see a "Visualization" tab. Clicking on it reveals a bar with a variety of chart types (line, bar, pie, table, scatter plot, etc.). Looker does a decent job of guessing the best chart type, but you can easily override it.
For our user growth report, a line chart is the perfect choice to show a trend over time.
Step 2: Customize Your Visualization
Click the "Edit" gear next to the visualization options to open up a panel of customization settings. Here you can tweak things like:
- Axis names and labels
- Chart colors and legends
- Displaying value labels on data points
- Reference lines for goals or targets
Taking a minute to properly label your chart makes it far more valuable for anyone who sees it.
Step 3: Save as a "Look"
Once you’re happy with your visualization, it's time to save it. Click the gear icon in the top right corner of the page and select "Save as a Look." Give it a descriptive name like "Monthly User Growth - Last 12 Months" and choose which folder to save it in.
Step 4: Add Your Look to a Dashboard
Now that your Look is saved, you can add it to a dashboard. From the same top-right gear menu, choose "Add to Dashboard."
You'll be prompted to select an existing dashboard or create a new one. Once you add it, you can enter the dashboard's "edit mode" to resize and rearrange your new tile until you have a layout that tells a clear story.
Repeat this process - Explore, Visualize, Save, Add to Dashboard - and you'll have a fully functional dashboard tracking your key metrics in no time.
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Two Key Tips for Success with Looker
1. Lean on Existing Dashboards First
Before you dive into building everything from scratch, see what dashboards your team has already built. Often, 90% of what you need is already available. You can click into any tile on a dashboard to explore its underlying query, clone it, and use it as a starting point for your own analysis.
2. Don't Be Afraid to Experiment
The beauty of the "Explore" environment is that you cannot break anything. You are just querying data, not changing it. So click around, add different dimensions and measures, play with filters, and see what you discover. It's the best way to learn what data is available to you.
Final Thoughts
Google Looker is an incredibly robust platform that provides consistent, reliable reporting for an entire organization. Its power comes from the LookML data model, though this can create a steep learning curve. The key for a business user is mastering the workflow: start in an Explore, build a query with dimensions and measures, visualize the result, and save it as a Look to a dashboard.
That manual process of searching for dimensions, building queries, and configuring charts is exactly what is disappearing with newer AI tools. With Graphed, we skip the need to learn a complex BI platform or wait for a data analyst. You simply connect your sources (like Google Analytics, Salesforce, or Shopify) and describe what you want to see in plain English. We turn your request, like "Create a dashboard showing our sales pipeline velocity and conversion rates," into a real-time, shareable dashboard for you in seconds.
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